Project Description
As Generative AI is everywhere around, we want to research its possibilities, how it can help SUSE, its employees and customers. The initial idea is to build solution based on Amazon Bedrock, to integrate our asset management tools and to be able to query the data and get the answers using human-like text.
Goal for this Hackweek
Populate all available data from SUSE Asset management tools (integrate with Jira Insight, Racktables, CloudAccountMetadata, Cloudquery,...) into the foundational model (e.g. Amazon Titan). Then make the foundational model able to answer simple queries like how many VMs are running in PRG2 or who are the owners of EC2 instances of t2 family.
This is only one of the ideas for GenAI we have. Most probably we will try to cover also another scenarios. If you are interested or you have any other idea how to utilize foundational models, let us know.
Resources
- https://aws.amazon.com/bedrock/
- https://github.com/aws-samples/amazon-bedrock-workshop
- Specifically for this hackweek was created AWS Account
ITPE Gen IA Dev (047178302800)accessible via Okta - whoever is interested, please contact me (or raise JiraSD ticket to be added to CLZ: ITPE Gen IA Dev) and use region us-west-2 (don't mind the typo, heh). - we have booked AWS engineer, expert on Bedrock on 2023-11-06 (1-5pm CET, meeting link) - anyone interested can join
Keywords
AI, GenAI, GenerativeAI, AWS, Amazon Bedrock, Amazon Titan, Asset Management
Looking for hackers with the skills:
ai genai generativeai aws amazontitan assetmanagement bedrock
This project is part of:
Hack Week 23
Activity
Comments
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over 1 year ago by mpiala | Reply
and recording of the workshop: Gen AI with AWS-20231106_130308-Meeting Recording.mp4
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over 1 year ago by vadim | Reply
@mpiala now that we all have some hands on experience with bedrock I suggest that you create a few workgroup sessions for tomorrow / Thursday and invite contributors. I'd split the work into three workstreams:
- Create infrastructure / pipelines that would deploy the project in a reproducible way
- Create a crawler that would parse the source data and populate a vector database (probably a lambda that can be triggered by cloudwatch)
- Create a backend that would query the vector DB, run inference and integrate with slack
For vector DB we can use something off the shelf, like pinecone.io - later we can move it to Athena or something else.
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